Classification of catchments for nitrogen using Artificial Neural Network Pattern Recognition and spatial data

O'Sullivan, Cherie M. and Ghahramani, Afshin ORCID: https://orcid.org/0000-0002-9648-4606 and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Pembleton, Keith ORCID: https://orcid.org/0000-0002-1896-4516 and Khan, Urooj and Tuteja, Narendra (2021) Classification of catchments for nitrogen using Artificial Neural Network Pattern Recognition and spatial data. Science of the Total Environment. ISSN 0048-9697


Abstract

In hydrological modelling, classification of catchments is a fundamental task for overcoming deficits in observational datasets. Most attention on this issue has focussed on identifying the catchments with similar hydrological responses for streamflow. Yet, effective methods for catchment classification are currently lacking in respect to Dissolved Inorganic Nitrogen (DIN), a water quality constituent that, at increasing concentrations, is threatening nutrient sensitive environment. Pattern recognition, using standard Artificial Neural Network algorithm is applied, as a novel approach to classify datasets that are considered to be suitable proxies for biological and anthropogenic drivers of observed DIN releases. Eleven gauged Great Barrier Reef (GBR) catchments within Queensland Australia are classified using spatial datasets extracted from ecosystem (e.g. original ecosystem responses to biogeographic, land zone, land form, and soil type attributes) and land use maps. To evaluate the performance of the examined spatial datasets as a proxy for deductive classification, the classification process is repeated inductively, using observed DIN and streamflow data from gauging stations. The ANN-PR method is seen to generate the same classification score format for the differing dataset types, and this facilitates a direct comparison for model output for observed data corroborations. The Kruskal-Wallis test for independence, at p > 0.05, identifies the deductive classification approach as a predictor for classification using DIN observations, which lacks an independence from each other at a p value of 0.01 and 0.02. This study concludes that an ANN-PR method can integrate the ecosystem and land use mapping data to deductively classify the GBR catchments into four regions that also have similar patterns of DIN concentrations. Due to the uniform availability of the mapping data, the findings provide a sound basis for further investigations into the transposing of knowledge from gauged catchments to ungauged areas.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online: 29 October 2021. Permanent restricted access to Accepted version, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Sustainable Agricultural Systems (1 Aug 2018 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Date Deposited: 09 Nov 2021 04:37
Last Modified: 17 Nov 2021 04:22
Uncontrolled Keywords: water quality, Great Barrier Reef, deductive inductive catchment classification, DIN, artificial intelligence, pattern recognition
Fields of Research (2008): 05 Environmental Sciences > 0502 Environmental Science and Management > 050204 Environmental Impact Assessment
04 Earth Sciences > 0406 Physical Geography and Environmental Geoscience > 040608 Surfacewater Hydrology
05 Environmental Sciences > 0502 Environmental Science and Management > 050206 Environmental Monitoring
Fields of Research (2020): 37 EARTH SCIENCES > 3707 Hydrology > 370701 Contaminant hydrology
41 ENVIRONMENTAL SCIENCES > 4105 Pollution and contamination > 410504 Surface water quality processes and contaminated sediment assessment
37 EARTH SCIENCES > 3707 Hydrology > 370704 Surface water hydrology
Socio-Economic Objectives (2008): D Environment > 96 Environment > 9606 Environmental and Natural Resource Evaluation > 960608 Rural Water Evaluation (incl. Water Quality)
D Environment > 96 Environment > 9605 Ecosystem Assessment and Management > 960506 Ecosystem Assessment and Management of Fresh, Ground and Surface Water Environments
Socio-Economic Objectives (2020): 18 ENVIRONMENTAL MANAGEMENT > 1803 Fresh, ground and surface water systems and management > 180306 Measurement and assessment of freshwater quality (incl. physical and chemical conditions of water)
Identification Number or DOI: https://doi.org/10.1016/j.scitotenv.2021.151139
URI: http://eprints.usq.edu.au/id/eprint/44102

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